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CLC number: TP391.4

On-line Access: 2012-01-19

Received: 2010-07-31

Revision Accepted: 2011-01-05

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.2 P.99-117

10.1631/jzus.C10a0728


PRISMO: predictive skyline query processing over moving objects


Author(s):  Nan Chen, Li-dan Shou, Gang Chen, Yun-jun Gao, Jin-xiang Dong

Affiliation(s):  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   cnasd715@zju.edu.cn, should@zju.edu.cn, cg@zju.edu.cn, gaoyj@zju.edu.cn, djx@zju.edu.cn

Key Words:  Spatio-temporal database, Moving object, Skyline


Nan Chen, Li-dan Shou, Gang Chen, Yun-jun Gao, Jin-xiang Dong. PRISMO: predictive skyline query processing over moving objects[J]. Journal of Zhejiang University Science C, 2012, 13(2): 99-117.

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author="Nan Chen, Li-dan Shou, Gang Chen, Yun-jun Gao, Jin-xiang Dong",
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%A Li-dan Shou
%A Gang Chen
%A Yun-jun Gao
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%J Journal of Zhejiang University SCIENCE C
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%I Zhejiang University Press & Springer
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A1 - Li-dan Shou
A1 - Gang Chen
A1 - Yun-jun Gao
A1 - Jin-xiang Dong
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EP - 117
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C10a0728


Abstract: 
skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over moving objects, however, is also important and requires more attention. In this paper, we propose a framework, namely PRISMO, for processing predictive skyline queries over moving objects that not only contain spatio-temporal information, but also include non-spatial dimensions, such as other dynamic and static attributes. We present two schemes, RBBS (branch-and-bound skyline with rescanning and repacking) and TPBBS (time-parameterized branch-and-bound skyline), each with two alternative methods, to handle predictive skyline computation. The basic TPBBS is further extended to TPBBSE (TPBBS with expansion) to enhance the performance of memory space consumption and CPU time. Our schemes are flexible and thus can process point, range, and subspace predictive skyline queries. Extensive experiments show that our proposed schemes can handle predictive skyline queries effectively, and that TPBBS significantly outperforms RBBS.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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